

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_ta import TaTokenizer
else:
       _SCREAMING_SNAKE_CASE	:    int       =     None


_SCREAMING_SNAKE_CASE	:    int       =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:    Dict       =     {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE	:    Optional[Any]       =     {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_SCREAMING_SNAKE_CASE	:    Any       =     {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}






class 			UpperCAmelCase__						(			_UpperCAmelCase			):






  """simple docstring"""


  a 	=   VOCAB_FILES_NAMES
  a 	=   PRETRAINED_VOCAB_FILES_MAP
  a 	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a 	=   ['input_ids', 'attention_mask']
  a 	=   TaTokenizer

  a 	=   []






  def __init__(				self		:						int      ,  __lowerCamelCase		:						int=None      ,  __lowerCamelCase		:						List[str]=None      ,  __lowerCamelCase		:						List[str]="</s>"      ,  __lowerCamelCase		:						str="<unk>"      ,  __lowerCamelCase		:						List[Any]="<pad>"      ,  __lowerCamelCase		:						List[Any]=100      ,  __lowerCamelCase		:						Optional[Any]=None      ,  **__lowerCamelCase		:						Optional[Any]      ,  )						->				List[str]:






       if extra_ids > 0 and additional_special_tokens is None:
            SCREAMING_SNAKE_CASE__       =       [f'''<extra_id_{i}>''' for i in range(__UpperCamelCase   )]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            SCREAMING_SNAKE_CASE__       =       len(set(filter(lambda __lowerCamelCase   : bool('''extra_id_''' in str(__UpperCamelCase   )   )      ,  __UpperCamelCase   )   )   )
            if extra_tokens != extra_ids:
                 raise ValueError(
                     f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                     ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                     ''' tokens'''   )

       super().__init__(
           __UpperCamelCase      ,  tokenizer_file=__UpperCamelCase      ,  eos_token=__UpperCamelCase      ,  unk_token=__UpperCamelCase      ,  pad_token=__UpperCamelCase      ,  extra_ids=__UpperCamelCase      ,  additional_special_tokens=__UpperCamelCase      ,  **__UpperCamelCase      ,  )

       SCREAMING_SNAKE_CASE__       =       vocab_file
       SCREAMING_SNAKE_CASE__       =       False if not self.vocab_file else True
       SCREAMING_SNAKE_CASE__       =       extra_ids






  @staticmethod
  def  lowercase_						(				__lowerCamelCase		:						str      ,  __lowerCamelCase		:						int      ,  __lowerCamelCase		:						str   )						->				Any:






       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            SCREAMING_SNAKE_CASE__       =       TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
                 return init_max_model_length
            elif init_max_model_length is None:
                 warnings.warn(
                     '''This tokenizer was incorrectly instantiated with a model max length of'''
                     f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                     ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                     ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                     f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                     f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                     f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                     ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                     ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''      ,  __UpperCamelCase      ,  )

       return max_model_length






  def  lowercase_						(				self		:						Tuple      ,  __lowerCamelCase		:						str      ,  __lowerCamelCase		:						Optional[str] = None   )						->				Tuple[str]:






       if not self.can_save_slow_tokenizer:
            raise ValueError(
                '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                '''tokenizer.'''   )

       if not os.path.isdir(__UpperCamelCase   ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
            return
       SCREAMING_SNAKE_CASE__       =       os.path.join(
           __UpperCamelCase      ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(__UpperCamelCase   ):
            copyfile(self.vocab_file      ,  __UpperCamelCase   )
            logger.info(f'''Copy vocab file to {out_vocab_file}'''   )

       return (out_vocab_file,)






  def  lowercase_						(				self		:						Tuple      ,  __lowerCamelCase		:						List[int]      ,  __lowerCamelCase		:						Optional[List[int]] = None   )						->				List[int]:
       SCREAMING_SNAKE_CASE__       =       token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
       else:
            SCREAMING_SNAKE_CASE__       =       token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a






  def  lowercase_						(				self		:						Optional[Any]      ,  __lowerCamelCase		:						List[int]      ,  __lowerCamelCase		:						Optional[List[int]] = None   )						->				List[int]:
       SCREAMING_SNAKE_CASE__       =       [self.eos_token_id]

       if token_ids_a is None:
            return len(token_ids_a + eos   ) * [0]
       return len(token_ids_a + eos + token_ids_a + eos   ) * [0]






  def  lowercase_						(				self		:						Any   )						->				List[str]:
       return list(
           set(filter(lambda __lowerCamelCase   : bool(re.search(r'''<extra_id_\d+>'''      ,  __UpperCamelCase   )   ) is not None      ,  self.additional_special_tokens   )   )   )







  def  lowercase_						(				self		:						List[Any]   )						->				Tuple:
       return [self.convert_tokens_to_ids(__UpperCamelCase   ) for token in self.get_sentinel_tokens()]




"""simple docstring"""



import cva
import numpy as np




class 		a_    :







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							float  ,       __UpperCamelCase      :							int  )		->Dict:






        '''simple docstring'''






        if k in (0.0_4, 0.0_6):
            _UpperCAmelCase        = k
            _UpperCAmelCase        = window_size
        else:
            raise ValueError("""invalid k value"""  )







    def __str__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        return str(self.k  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  )		->tuple[cva.Mat, list[list[int]]]:






        '''simple docstring'''






        _UpperCAmelCase        = cva.imread(__UpperCamelCase  ,       0  )
        _UpperCAmelCase     ,_UpperCAmelCase        = img.shape
        _UpperCAmelCase        = []
        _UpperCAmelCase        = img.copy()
        _UpperCAmelCase        = cva.cvtColor(__UpperCamelCase  ,       cva.COLOR_GRAY2RGB  )
        _UpperCAmelCase     ,_UpperCAmelCase        = np.gradient(__UpperCamelCase  )
        _UpperCAmelCase        = dx**2
        _UpperCAmelCase        = dy**2
        _UpperCAmelCase        = dx * dy
        _UpperCAmelCase        = 0.0_4
        _UpperCAmelCase        = self.window_size // 2
        for y in range(__UpperCamelCase  ,       h - offset  ):
            for x in range(__UpperCamelCase  ,       w - offset  ):
                _UpperCAmelCase        = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _UpperCAmelCase        = (wxx * wyy) - (wxy**2)
                _UpperCAmelCase        = wxx + wyy
                _UpperCAmelCase        = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r]  )
                    color_img.itemset((y, x, 0)  ,       0  )
                    color_img.itemset((y, x, 1)  ,       0  )
                    color_img.itemset((y, x, 2)  ,       2_55  )
        return color_img, corner_list


if __name__ == "__main__":
 a       :						List[Any]       		=		HarrisCorner(0.04, 3)
 a		,			a       :						List[Any]       		=		edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)